Bayesian Analysis of Student t Linear Regression with Unknown Change-Point and Application to Stock Data Analysis

Jin-Guan Lin (), Ji Chen and Yong Li ()

Computational Economics, 2012, vol. 40, issue 3, 203-217

Abstract: This article devotes to studying the variance change-points problem in student t linear regression models. By exploiting the equivalence of the student t distribution and an appropriate scale mixture of normal distributions, a Bayesian approach combined with Gibbs sampling is developed to detect the single and multiple change points. Some simulation studies are performed to display the process of the detection and investigate the effects of the developed approach. Finally, for illustration, the Dow Jones index closed data of U.S. market are analyzed and three variance change-points are detected. Copyright Springer Science+Business Media, LLC. 2012

Keywords: Bayesian method; Student t regression model; Variance change-point; Gibbs sampler (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10614-011-9305-8 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:40:y:2012:i:3:p:203-217

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-011-9305-8

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().